Summary of Dual-decoupling Learning and Metric-adaptive Thresholding For Semi-supervised Multi-label Learning, by Jia-hao Xiao et al.
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
by Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to semi-supervised multi-label learning (SSMLL), which addresses the challenges of generating high-quality pseudo-labels in SSMLL. The authors develop a dual-perspective method that decouples the learning of correlative and discriminative features, refining both model predictions and pseudo-label generation. Additionally, they introduce a metric-adaptive thresholding strategy to estimate optimal class-wise thresholds for maximizing pseudo-label performance on labeled data. Experimental results demonstrate state-of-the-art performance on multiple benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from lots of data without needing labels. It’s like labeling pictures as “dog” or “cat”, but this time, it’s for many things at once (like colors, shapes, and textures). The authors created a new way to make the machine learning process better by mixing up what it learns about patterns and details. They also found a way to get the right labels from unlabeled data. This makes machines super good at recognizing lots of things! |
Keywords
* Artificial intelligence * Machine learning * Semi supervised
